Dependency Parsing with Backtracking using Deep Reinforcement Learning

2022-06-28 · Franck Dary, Maxime Petit, Alexis Nasr

Greedy algorithms for NLP such as transition based parsing are prone to error propagation. One way to overcome this problem is to allow the algorithm to backtrack and explore an alternative solution in cases where new evidence contradicts the solution explored so far. In order to implement such a behavior, we use reinforcement learning and let the algorithm backtrack in cases where such an action gets a better reward than continuing to explore the current solution. We test this idea on both POS tagging and dependency parsing and show that backtracking is an effective means to fight against error propagation.

📄 PDF Abstract BibTeX arXiv:2206.13914

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Deep Reinforcement LearningDependency ParsingPOSPOS Tagging reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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